- Tytuł:
- Towards textual data augmentation for neural networks: synonyms and maximum loss
- Autorzy:
-
Jungiewicz, Michał
Smywiński-Pohl, Aleksander - Powiązania:
- https://bibliotekanauki.pl/articles/305750.pdf
- Data publikacji:
- 2019
- Wydawca:
- Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
- Tematy:
-
deep learning
data augmentation
neural networks
natural language processing
sentence classification - Opis:
- Data augmentation is one of the ways to deal with labeled data scarcity and overfitting. Both of these problems are crucial for modern deep-learning algorithms, which require massive amounts of data. The problem is better explored in the context of image analysis than for text; this work is a step forward to help close this gap. We propose a method for augmenting textual data when training convolutional neural networks for sentence classification. The augmentation is based on the substitution of words using a thesaurus as well as Princeton University's WordNet. Our method improves upon the baseline in most of the cases. In terms of accuracy, the best of the variants is 1.2% (pp.) better than the baseline.
- Źródło:
-
Computer Science; 2019, 20 (1); 57-83
1508-2806
2300-7036 - Pojawia się w:
- Computer Science
- Dostawca treści:
- Biblioteka Nauki
Artykuł